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Selecting Seed Nodes for Influence Maximization in Dynamic Networks

  • Conference paper
Complex Networks VI

Part of the book series: Studies in Computational Intelligence ((SCI,volume 597))

Abstract

This paper proposes a method for solving influence maximization problem in a dynamic network. In our method, a node that increases its influence most will be searched and it is added to the seed nodes incrementally. Since exact computation of influence of a node is #P-Hard, we employ heuristics for approximate computation. The results of our experiments show that our method is more effective than the methods based on centralities for dynamic networks, especially when the networks exhibit community structures.

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References

  1. Blondel, V.D., Guillaume, J.-L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment 2008(10), P10008 (2008)

    Article  Google Scholar 

  2. Berger-Wolf, T.Y.: Maximizing the extent of spread in a dynamic network. DIMACS Technical Report 2007-20, 10 pages (2007)

    Google Scholar 

  3. Bai, W.-J., Zhou, T., Wang, B.-H.: Immunization of susceptible–infected model on scale-free networks. Physica A: Statistical Mechanics and its Applications 384(2), 656–662 (2007)

    Article  Google Scholar 

  4. Cheng, S., Shen, H., Huang, J., Zhang, G., Cheng, X.: Staticgreedy: solving the scalability-accuracy dilemma in influence maximization. In: Proceedings of the 22nd ACM International Conference on Conference on Information & Knowledge Management, pp. 509–518 (2013)

    Google Scholar 

  5. Chen, W., Wang, C., Wang, Y.: Scalable influence maximization for prevalent viral marketing in large-scale social networks. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1029–1038 (2010)

    Google Scholar 

  6. Grindrod, P., Parsons, M.C., Higham, D.J., Estrada, E.: Communicability across evolving networks. Physical Review E 83(4), 046120 (2011)

    Article  Google Scholar 

  7. Holme, P., Saramäki, J.: Temporal networks. Physics Reports 519(3), 97–125 (2012)

    Article  Google Scholar 

  8. Isella, L., Stehlé, J., Barrat, A., Cattuto, C., Pinton, J.-F., Van den Broeck, W.: What’s in a crowd? analysis of face-to-face behavioral networks. Journal of Theoretical Biology 271(1), 166–180 (2011)

    Article  MathSciNet  Google Scholar 

  9. Jung, K., Heo, W., Chen, W.: Irie: Scalable and robust influence maximization in social networks. In: ICDM, pp. 918–923 (2012)

    Google Scholar 

  10. Jo, H.-H., Pan, R.K., Kaski, K.: Emergence of bursts and communities in evolving weighted networks. PloS One 6(8), e22687 (2011)

    Article  Google Scholar 

  11. Jiang, Q., Song, G., Cong, G., Wang, Y., Si, W., Xie, K.: Simulated annealing based influence maximization in social networks. In: AAAI, pp. 127–132 (2011)

    Google Scholar 

  12. Katz, L.: A new status index derived from sociometric analysis. Psychometrika 18(1), 39–43 (1953)

    Article  MATH  Google Scholar 

  13. Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 137–146 (2003)

    Google Scholar 

  14. Newman, M.E.J.: Modularity and community structure in networks. Proceedings of the National Academy of Sciences 103(23), 8577–8582 (2006)

    Article  Google Scholar 

  15. Peyrard, N., Sabbadin, R.: Evaluation of the expected size of a sir epidemics on a graph. UBIAT Resarch Report, RR-2012-1 (2012)

    Google Scholar 

  16. Vanhems, P., Barrat, A., Cattuto, C., Pinton, J.-F., Khanafer, N., Régis, C., Kim, B.-A., Comte, B., Voirin, N.: Estimating potential infection transmission routes in hospital wards using wearable proximity sensors. PloS One 8(9), 73970 (2013)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, Graduate School of Information Science and Engineering, Tokyo Institute of Technology, W8-59 2-12-1 Ookayama, Meguro, Tokyo, 152-8552, Japan

    Shogo Osawa & Tsuyoshi Murata

Authors
  1. Shogo Osawa
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  2. Tsuyoshi Murata
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Corresponding author

Correspondence to Shogo Osawa .

Editor information

Editors and Affiliations

  1. Dip. di Ingegneria Elettrica, Elettronica e Informatica, University of Catania, Catania, Italy

    Giuseppe Mangioni

  2. Dept. of Engineering Mathematics, University of Bristol, Clifton, United Kingdom

    Filippo Simini

  3. New York Hall of Science, New York, New York, USA

    Stephen Miles Uzzo

  4. College of Information Sciences and Technology, Pennsylvania State University, University Park, New York, USA

    Dashun Wang

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© 2015 Springer International Publishing Switzerland

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Osawa, S., Murata, T. (2015). Selecting Seed Nodes for Influence Maximization in Dynamic Networks. In: Mangioni, G., Simini, F., Uzzo, S., Wang, D. (eds) Complex Networks VI. Studies in Computational Intelligence, vol 597. Springer, Cham. https://doi.org/10.1007/978-3-319-16112-9_9

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  • DOI: https://doi.org/10.1007/978-3-319-16112-9_9

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16111-2

  • Online ISBN: 978-3-319-16112-9

  • eBook Packages: EngineeringEngineering (R0)

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